1. Field of the Invention
The present invention generally relates to a method for reducing the noise equivalent temperature difference (NETD) associated with imaging devices including a focal plane array of micro-cantilevers and a charged-coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) imager, wherein the pixel density within the imager is greater than the number of micro-cantilevers within the camera.
2. Description of the Related Art
Traditional thermal cameras employ the thermo-electric effect to detect infrared (IR) signals via either current or voltage changes resulting from the temperature rise within a pixilated semiconductor. However, presently known technologies have several major deficiencies.
The sensitivity of IR cameras is fundamentally limited by electron thermal noise. Cooling devices are typically employed to mitigate electron thermal noise so as to improve sensitivity; however, this increases the power requirements and bulk of a camera system.
The fabrication of cooled IR cameras is both complex and costly because of the electrical interconnects required between pixels and integrated scanning readout electronics. Accordingly, such cameras are too costly for most commercial applications and too difficult to scale, thus limiting pixel density and array size which constrain image resolution. Furthermore, contact between electrical readout and sensor elements inevitably reduces sensitivity because of thermal signal leaks.
Uncooled thermal imagers avoid some of the problems of cooled devices by employing passive thermal bending and optical readout. Uncooled thermal imagers include bi-material micro-cantilever devices described by Ishizuya et al. in U.S. Pat. No. 6,835,932, Suzuki et al. in U.S. Pat. No. 6,469,301, Ishizuya et al. in U.S. Pat. No. 6,339,219, Thundat et al. in U.S. Pat. No. 6,118,124, Ishizuya et al. in U.S. Pat. No. 6,080,988, and Fisher in U.S. Pat. No. 5,929,440.
In Suzuki, an array of bi-material micro-cantilevers is described for infrared imaging based upon the direct conversion of an infrared signal to a visible signal. This approach includes passive sensors without electrical contacts, thus eliminating both electron thermal noise and pixel-level driving circuitry. However, the sensitivity of this and similar devices is highly dependent on optical readout methods and signal processing algorithms.
FIG. 1 shows an exemplary bi-material cantilever (BC) detector described by Ishizuya et al. (U.S. Pat. No. 6,339,219) which includes an infrared lens system 2, an infrared detection array 3, a first lens system 4, an aperture plate 5, a second lens system 6, and an imager 7 arranged in the order described. Within the front end of the apparatus, rays from a source 1 pass through the infrared lens system 2 and are thereafter directed onto the infrared detection array 3. The infrared detection array 3 includes a focal plane array 11 of micro-cantilever pixels 13 which is mechanically responsive to the thermal loading caused by the infrared rays. Within the back end of the apparatus, micro-cantilever pixels 13 reflect the incident light 9 from a visible light source 8, one example being a light emitting diode (LED), so that the reflected light 10 passes through the first lens system 4 which compresses the reflected light 10 allowing it to pass through a hole along the aperture plate 5. The reflected light 10 then passes through a second lens system 6 which expands the reflected light 10 so as to impinge a focal plane array 12 of receptor pixels 14 within the imager 7, examples being a complementary metal oxide semiconductor (CMOS) device or charged-coupled device (CCD). Thereafter, the resultant image is communicated to a video display device.
Low-cost CMOS or CCD imagers 7 facilitate a significantly larger number of receptor pixels 14 than the number of micro-cantilever pixels 13 within the infrared detection array 3. As such, ratios of 4-to-1, 9-to-1, 16-to-1 and higher are possible. FIG. 2 shows a schematic representation wherein the imaging device has four receptor pixels 14 for every one micro-cantilever pixel 13.
The system noise within an imaging device, which directly influences image quality, is limited by the shot noise inherent to CMOS and CCD imagers. For example, the RMS (root-mean square) fluctuation, nrms, of signal electrons caused by shot noise is equal to √{square root over (n)}, where n is the number of signal electrons generated within the CMOS or CCD well. As a function of the total signal, the shot noise <is> is equal to
                                          〈                          i              s                        〉                    =                                                    n                            n                        =                          1                              n                                                    ,                            (        1        )            where <is> can be expressed in terms of a percent (%).
Shot noise is a critical parameter because it facilitates a reduction in the noise equivalent temperature difference (NETD) for the overall imaging device. NETD is the ratio of system noise to system responsivity, which is equal to
                              NETD          =                                                    〈                                  i                  s                                〉                            ℜ                        =                          1                              ℜ                ⁢                                  n                                                                    ,                            (        2        )            where  is the system responsivity with units of percent signal change per degree Kelvin change in background temperature. Equation (2) clearly shows that NETD may be improved by either decreasing the shot noise (<is>) or increasing the responsivity () of the detector.
Shot noise may be reduced by increasing the number of signal electrons as suggested by the inverse square root dependence in Equation (2). Signal electrons are increased by collecting more of them within the well of each pixel along the focal plane array within the CMOS or CCD. This approach maximizes n and minimizes n−1/2 for a given design. However, the welt capacity of CMOS or CCD imagers is currently limited to approximately 5×105 electrons.
Two hardware independent methods, namely, spatial and temporal averaging, are known within the art to reduce shot noise by exploiting the statistical nature of shot noise. Shot noise has a Poisson distribution in both space and time such that the fluctuations at each pixel are independent of neighboring pixels within the same frame and independent between sequential frames for a given pixel.
Spatial averaging is a numerical method whereby the intensities for a fixed number of adjacent pixels within each group are combined, averaged, and assigned to a single pixel within the group. The resulting image has a resolution lowered by a factor of the number of pixels within the group. For example, FIG. 3 shows a 9-by-9 focal plane array 12 of receptor pixels 14, wherein each group includes nine pixels with intensities I1-I9 before spatial averaging. After spatial averaging, the numerical average of the group is assigned to the centermost pixel or the spatial averaged pixel 15. The resultant image after averaging has a resolution that is one-ninth that before averaging. Accordingly, spatial averaging lowers image resolution and causes significant loss of image detail.
Temporal averaging is another numerical method whereby the intensities, I1(t1), I1(t2), and I1(t3) in the example in FIG. 4, for the same pixel are averaged over a fixed number of sequential frames and thereafter assigned to the temporal averaged pixel 16 within one frame. In this method, averaging may include a combining average, whereby several frames are averaged to form one new frame and the other frames are discarded, as shown in FIG. 4, or a rolling average, whereby each frame is equal to the average of itself and several previous frames. The combining average method reduces the frame rate by the inverse of the number of frames averaged, thus effectively slowing detector speed. The rolling average method maintains the original frame rate; however, the successive averaging of intensities filters the image, thus reducing resolution.
Gindele et al. in U.S. Pat. Nos. 6,718,068 and 6,681,054, and Smith in U.K. Patent No. 2,272,285 describe other noise reduction methods for digital images. Both methods act as noise “filters” by attempting to correct the intensity of “noisy” pixels via numerical weighting and averaging approaches based on the intensity of neighboring pixels, rather than the combination of pixels.
Therefore, what is required is a noise reduction method which reduces the noise equivalent temperature difference (NETD) associated with an imaging device without compromising the resolution and quality of the image captured by the CMOS or CCD device.